Spectral techniques in analytical chemistry are often affected by baselines in practical
implementation. Without baseline correction, the accuracy of the qualitative/quantitative
analytical results may degrade substantially. Sparse representation has been applied to
baseline correction recently, which can provide state-of-the-art performance. However, it
suffers from possible performance degradation when realized using l 1-norm approximation.
To significantly improve the performance for baseline correction, a sparse Bayesian learning …